1. Field of the Invention
The present invention relates to a method of signal processing of electrograms for use in medical devices. The present invention additionally relates to a system for receiving and analyzing such signals.
2. Technical Background
Electrograms are a class of electrical signals, which are representations of underlying physiological processes. They are obtained through the medium of electrochemical, electromechanical or electrooptical sensors. Electrograms are particularly useful to monitor a patient's or subject's physiological state, diagnose illnesses and devise proper therapeutic strategies.
A particularity of this class of signals is their high sensitivity to environmental noise and other artifacts. Common artifacts are often of large amplitude as compared to the electrogram itself, which is typically a signal of rather small amplitude (μV or mV). Contamination by artifacts can significantly reduce the diagnostic and therapeutic utility of an electrogram. Therefore, there is a profound need in the medical community for signal processing techniques that remove these artifacts from EGs prior to their interpretation.
One example of a widely used electrogram is the Electroencephalogram (EEG), a measure of electrical brain activity. The EEG signal provides researchers with a noninvasive insight into the intricacy of the human brain. It is a valuable tool for clinicians in numerous applications, from the diagnosis of neurological disorders, to the clinical monitoring of depth of anesthesia. For an awake healthy subject, the normal EEG amplitude is in the order of 20-50 μV, making this signal very susceptible to various artifacts, which cause problems for its analysis and interpretation. In current data acquisition, eye movement and blinks related artifacts, as well as other electrophysiological contaminating signals (e.g., heart and muscle activity, head and body movements), are often the most dominant artifacts. Eye movements and blinks produce a large electrical signal around the eyes (in the order of mV), known as Electrooculogram (EOG), which spreads across the scalp and contaminates the EEG. These contaminating potentials are commonly referred to as Ocular Artifacts (OAs). Another common large-amplitude artifact (in the order mV) in the EEG signal is Electrocardiogram (ECG), which originates from electrical activity of the heart. In addition, movement and muscle activity artifacts, commonly referred to as Electromyogram (EMG), can be of very large amplitude and contain high frequencies.
There have been many attempts to successfully remove artifacts and noise from EGs and other biosignals. However, this task has proven to be a constant challenge in the signal-processing field. The rejection of epochs contaminated with artifacts usually leads to a substantial loss of data. Asking subjects not to produce artifacts is often inappropriate or inadequate. In addition, the fact that a subject needs to concentrate on fulfilling these requirements might by itself influence the interpretation of the EG. In the past, significant efforts have been made among researchers to find a way to successfully remove large-amplitude artifacts from EGs. An appropriate example for prior attempts are the techniques for removal of OAs from the EEG signal.
For example, time-domain regression methods have been widely used for removal of OAs from the EEG. These techniques involve the subtraction of some portion of the recorded Electrooculogram (EOG) from the EEG. They assume that the propagation of ocular potentials is volume conducted, frequency independent and without any time delay. Others argued that the scalp is not a perfect volume conductor, and thus, attenuates some frequencies more than others. Consequently, frequency-domain regression was proposed. However, neither time nor frequency regression techniques take into account the propagation of the brain signals into the recorded EOG. Thus a portion of relevant EEG signal is always cancelled out along with the artifact. Moreover, regression techniques mainly use different correction coefficients for different type of artifacts, e.g., eye blinks versus eye movements. They also heavily depend on the regressing artifact channel, in this case EOG channel. Eventually, more sophisticated regression methods were proposed for simultaneous correction of different artifacts, e.g., blinks and eye movement artifacts. Also, the influence of the EEG-to-EOG propagation has been somewhat minimized by these methods.
In an attempt to overcome the problem of the EEG-to-EOG propagation, a multiple source eye correction method has been proposed. In this method, the OA was estimated based on the source eye activity rather than the EOG signal. The method involves obtaining an accurate estimate of the spatial distribution of the eye activity from calibration data, which is a rather difficult task. Due to its de-correlation efficiency, the principal component analysis (PCA) has been applied for OA removal from the multi-channel EEG and it outperformed the previously mentioned methods. However, it has been shown that PCA cannot fully separate OAs from the EEG when comparable amplitudes are encountered.
Recently, independent component analysis (ICA) has demonstrated a superior potential for the removal of a wide variety of artifacts from the EEG, even in a case of comparable amplitudes. ICA simultaneously and linearly unmixes multi-channel scalp recordings into independent components, which are often physiologically plausible. Also, there is no need for a reference channel corresponding to each artifact source. However, ICA artifact removal is not yet fully automated and requires visual inspection of the independent components in order to decide of their removal.
Other attempts have applied different adaptive signal processing techniques. The performance of these methods also relies on minimal contamination of the EG of interest by the reference artifactual signal. These prior attempts have achieved only partial success because of being based on signal processing techniques which are not optimal for the analysis of a particular EG signal of interest. Electrograms such as the EEG are often characterized by their non-stationary nature, i.e., their time-frequency characteristics can significantly change over time. In addition, artifacts are often transitory in nature (i.e., localized in time), which cannot be captured by traditional spectral analysis. Therefore, it is natural to explore signal processing techniques that can capture these non-stationary events.
It is therefore an object of the present invention to develop a signal processing method utilizing time-frequency transforms, such as wavelet transforms, for the purpose of artifact removal from EGs. These transforms decompose a signal in both time and frequency domains, and therefore, are well suited for non-stationary signal analysis. As a result, dissimilar signal features are well localized both in time and frequency, which potentially provides a good separation between the signal of interest and artifacts. This particularly applies to large-amplitude artifacts corrupting EGs.
It is further an object of the present invention to use a method of signal analysis, which utilizes wavelet denoising. However, unlike in the present invention, conventional wavelet denoising is used for the removal of an additive white Gaussian noise (AWGN) or colored (i.e., correlated) noise from smooth and coherent signals, assuming a relatively high signal-to-noise ratio (SNR). In contrast to conventional denoising, the present invention applies to large-amplitude transient artifacts from not necessarily smooth, coherent or stationary signals (e.g., EEG).
It is still further an object of the present invention to utilize a method of an over-complete (i.e., redundant) time-frequency transform. Over-complete transforms yield more coefficients in each time-frequency span of the transform, which provides a better means for statistical analysis when deriving thresholds. Also, the resulting denoised signals are smoother, i.e., without introduced ringing artifacts.